The problem is wages, not jobs.
Image: Pat David / Flickr
As an economist, my own research agenda is based on the premise that guiding AI to beneficial results is the most important problem facing our generation. Since Daron Acemoglu and I are close thinkers on this subject, I will take this opportunity to emphasize and extend its essential message.
Acemoglu is right on three key points. First, core AI technologies are indeed advancing rapidly and becoming more and more powerful. Improvements in machine learning, in particular, such as deep learning techniques that have made such rapid progress on the Imagenet dataset, are increasingly affecting the economy. In good applications, the gain can be significant. Adoption is still in its early stages – only 1.3% of businesses in the United States have adopted robotics, for example – but the numbers are growing rapidly. Second, the societal implications of AI run deep, especially when it comes to the future of work, our individual freedoms, and our democracy. Third, and most importantly, the results are not predetermined.
The most common question people ask me when I talk about AI is a version of: what will AI do to society? But that’s not the right question to ask; it erases our agency.
This last point should not be taken for granted. The most common question people ask me when I talk about AI is a version of: what will AI do to society? But that’s not the right question to ask; it erases our agency. Acemoglu deserves special credit not only for diagnosing the challenges created by these technologies, but also for proposing a set of specific solutions. (In fact, Acemoglu has long been one of the strongest and most rigorous advocates of the idea that we can and must lead the course of technical change. He published an influential article making this argument nearly twenty years.)
Regarding Acemoglu’s diagnosis, I would like to highlight the following details. When it comes to the effect of AI on the workforce, the real challenge is in wages, not jobs. As employment has grown over the past forty years, the real wages of Americans with a high school diploma or less have fallen. Tyler Cowen and others have argued that this is proof of a lack of technological advancement, but overall GDP and GDP per capita have also increased, and 2019 saw a record number of billionaires. Drawing on the work of Acemoglu as well as David Autor, Lawrence Katz, Melissa Kearney, Frank Levy and Richard Murnane, Andrew McAfee and I argued that technological advancements were not incompatible with declining prices. wages for some or even a large part of the workforce.
The magnitude of these changes, and the most important to come, is enormous. The value of all human capital in the United States – the sum of the skills, experience, education and know-how of American workers – is probably around $ 240 trillion. This implies that if our decisions alter the trajectory of the effects of technology on the US economy enough to cause even a 10% change in that value, it would be worth more than a full year of GDP (currently $ 21 trillion). .
When it comes to AI’s effect on democracy, we should be concerned about the growing polarization and activation of Orwellian levels of surveillance. As Marshall van Alstyne and I wrote, it’s precisely because digital technologies make it easier for us to find content and people we love that they can also separate and polarize us. These technologies can also massively amplify the state’s power to monitor the words and actions of its citizens, giving it the power not only to silence critics, but even to shape their thoughts. The implications are increasingly recognized by world leaders; as even Vladimir Putin said, “Whoever becomes the leader in this area will become the ruler of the world.” In the wrong hands, the result may be what Jean Tirole calls a digital dystopia.
But no outcome is inevitable. We have the ability to lead AI, just like we can lead other types of technical change. Let me focus on three groups who can and should play a role in shaping AI for good: technologists, managers, and policy makers.
In contrast, when technology complements work, wages tend to rise, creating more widely shared prosperity.
“It’s remarkable,” Acemoglu writes, “that a lot of AI research still focuses on applications that automate tasks.” It is an underestimated problem. While it may be profitable to automate jobs, thereby substituting technology for human labor, in the long run the greatest gains come from the complementarity of humans and the ability to create value in new ways. Moreover, when technology takes the place of labor, pitting humans against machines, it tends to lower wages and lead to a greater concentration of wealth.
In contrast, when technology complements work, wages tend to rise, creating more widely shared prosperity. (In addition to replacing or supplementing the workforce, Tom Mitchell and I describe four additional considerations about how technology will affect wages: price elasticity, income elasticity, elasticity of labor supply work and business process redesign. will pay higher wages.) This is why McAfee and I argued that “in medicine, law, finance, retail, manufacturing and even scientific discoveries, the key to winning the race is not to compete with machines but to compete with machines â. Indeed, at a major AI conference three years ago, I directly called on the gathered technologists to reorient their work from replicating and automating human labor to increasing it.
Fortunately, a growing number of researchers are working to use AI to augment humans rather than replace them. Take Cresta, an AI start-up that I recommend. While many competitors strive to develop fully automated chatbots that interact directly with potential customers, Cresta is keeping one person up to date. The system works alongside human operators, looking for opportunities to suggest ways to improve the dialogue, suggesting a product or service upgrade, offering a price reminder or coaching on the tone and tactics. Via a series of A / B tests, Cresta found that this approach created demonstrable benefits for customers and also appeared to benefit newer and less skilled workers, helping to close the pay gap and reduce inequalities.
Managers, entrepreneurs, workers’ representatives and other business leaders also have a key role to play. Like technologists, they too often look at existing processes and ask the easy question: How can machines do what humans do now? The most difficult but ultimately the most valuable question is different: how can technology and people work together to create new sources of value? The more powerful and general the technology, the more important it is to rethink the work. As Paul David, Warren Devine, Jr., and others have documented, significant productivity gains from electricity in manufacturing did not happen until executives fundamentally reinvented how factories were organized. , a process that took thirty years or more – long enough for a generation. of manager to retire and be replaced by fresher thinking. Likewise, modern businesses are subject to similar dynamics, creating a lull in productivity as intangible investments in organizational and human capital are created to complement new technologies like AI.
Policymakers can help each of the first two groups make better decisions by changing the incentives. Take taxation. A key lesson from public finances is that we tend to get less of whatever we tax more. The current US tax system treats capital more favorably than labor. If two entrepreneurs each have a billion dollar idea for using AI, the one who employs the most labor will likely be taxed more than the one with the most capital. To the extent that labor income is more widely distributed, this element of our tax system discourages shared prosperity. This is a powerful argument for leveling the playing field. In fact, there is also a good argument that we should go further as far as we believe there are positive externalities for employment. (2015 book by Robert PutnamOur Children: The American Dream in Crisis describes the negative effects of unemployment, while the 2020 study by Anne Case and Angus DeatonDeaths of Despair and the Future of Capitalism documents the increase in deaths from suicide, drug addiction and alcoholism in demographic groups most affected by declining demand for labor.) Depending on the strength of these externalities, they could reverse the classic results suggesting that taxes on capital should be lower than taxes on labor.
As the power of AI grows, our values ââbecome more and more important. It is incumbent upon each of us to think deeply about the kind of society we want.
This list of agents of change is of course not exhaustive. Economists also have a role to play in guiding the debate, as do the general public. As the power of AI grows, our values ââbecome more and more important. It is up to each of us to think deeply about the kind of society we want. It is crucial to bring these issues to the forefront of popular discussions.
In the face of all these possibilities for change, I remain a conscious optimist. Acemoglu notes that we are far from a consensus on how to move forward. If it is a challenge, it is also an opportunity to forge a shared vision. But our window is short. If wealth and power become more and more concentrated, and democracy weakens further, we will reach a point of no return. We can and must act now to prevent this from happening and to redirect AI for the benefit of many, not just a few.